Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage

摘要

This letter proposes a novel sparsity-aware adaptive filtering scheme andalgorithms based on an alternating optimization strategy with shrinkage. Theproposed scheme employs a two-stage structure that consists of an alternatingoptimization of a diagonally-structured matrix that speeds up the convergenceand an adaptive filter with a shrinkage function that forces the coefficientswith small magnitudes to zero. We devise alternating optimization least-meansquare (LMS) algorithms for the proposed scheme and analyze its mean-squareerror. Simulations for a system identification application show that theproposed scheme and algorithms outperform in convergence and tracking existingsparsity-aware algorithms.